Dataset Information

Identifying Protein-metabolite Networks Associated with COPD Phenotypes.

ABSTRACT: Chronic obstructive pulmonary disease (COPD) is a disease in which airflow obstruction in the lung makes it difficult for patients to breathe. Although COPD occurs predominantly in smokers, there are still deficits in our understanding of the additional risk factors in smokers. To gain a deeper understanding of the COPD molecular signatures, we used Sparse Multiple Canonical Correlation Network (SmCCNet), a recently developed tool that uses sparse multiple canonical correlation analysis, to integrate proteomic and metabolomic data from the blood of 1008 participants of the COPDGene study to identify novel protein-metabolite networks associated with lung function and emphysema. Our aim was to integrate -omic data through SmCCNet to build interpretable networks that could assist in the discovery of novel biomarkers that may have been overlooked in alternative biomarker discovery methods. We found a protein-metabolite network consisting of 13 proteins and 7 metabolites which had a -0.34 correlation (p-value = 2.5 × 10-28) to lung function. We also found a network of 13 proteins and 10 metabolites that had a -0.27 correlation (p-value = 2.6 × 10-17) to percent emphysema. Protein-metabolite networks can provide additional information on the progression of COPD that complements single biomarker or single -omic analyses.

SUBMITTER: Mastej E

PROVIDER: S-EPMC7241079 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Identifying Protein-metabolite Networks Associated with COPD Phenotypes.

Mastej Emily E Gillenwater Lucas L Zhuang Yonghua Y Pratte Katherine A KA Bowler Russell P RP Kechris Katerina K

Metabolites 20200325 4

Chronic obstructive pulmonary disease (COPD) is a disease in which airflow obstruction in the lung makes it difficult for patients to breathe. Although COPD occurs predominantly in smokers, there are still deficits in our understanding of the additional risk factors in smokers. To gain a deeper understanding of the COPD molecular signatures, we used Sparse Multiple Canonical Correlation Network (SmCCNet), a recently developed tool that uses sparse multiple canonical correlation analysis, to inte ...[more]

PMID: 32218378

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Project description:Chronic obstructive pulmonary disease (COPD) is characterized by expiratory airflow limitation and symptoms such as shortness of breath. Although many studies have demonstrated dysregulated microRNA (miRNA) and gene (mRNA) expression in the pathogenesis of COPD, how miRNAs and mRNAs systematically interact and contribute to COPD development is still not clear. To gain a deeper understanding of the gene regulatory network underlying COPD pathogenesis, we used Sparse Multiple Canonical Correlation Network (SmCCNet) to integrate whole blood miRNA and RNA-sequencing data from 404 participants in the COPDGene study to identify novel miRNA-mRNA networks associated with COPD-related phenotypes including lung function and emphysema. We hypothesized that phenotype-directed interpretable miRNA-mRNA networks from SmCCNet would assist in the discovery of novel biomarkers that traditional single biomarker discovery methods (such as differential expression) might fail to discover. Additionally, we investigated whether adjusting -omics and clinical phenotypes data for covariates prior to integration would increase the statistical power for network identification. Our study demonstrated that partial covariate adjustment for age, sex, race, and CT scanner model (in the quantitative emphysema networks) improved network identification when compared with no covariate adjustment. However, further adjustment for current smoking status and relative white blood cell (WBC) proportions sometimes weakened the power for identifying lung function and emphysema networks, a phenomenon which may be due to the correlation of smoking status and WBC counts with the COPD-related phenotypes. With partial covariate adjustment, we found six miRNA-mRNA networks associated with COPD-related phenotypes. One network consists of 2 miRNAs and 28 mRNAs which had a 0.33 correlation (p = 5.40E-12) to forced expiratory volume in 1 s (FEV1) percent predicted. We also found a network of 5 miRNAs and 81 mRNAs that had a 0.45 correlation (p = 8.80E-22) to percent emphysema. The miRNA-mRNA networks associated with COPD traits provide a systems view of COPD pathogenesis and complements biomarker identification with individual miRNA or mRNA expression data.

Project description:BackgroundProtein complexes formed by non-covalent interaction among proteins play important roles in cellular functions. Computational and purification methods have been used to identify many protein complexes and their cellular functions. However, their roles in terms of causing disease have not been well discovered yet. There exist only a few studies for the identification of disease-associated protein complexes. However, they mostly utilize complicated heterogeneous networks which are constructed based on an out-of-date database of phenotype similarity network collected from literature. In addition, they only apply for diseases for which tissue-specific data exist.MethodsIn this study, we propose a method to identify novel disease-protein complex associations. First, we introduce a framework to construct functional similarity protein complex networks where two protein complexes are functionally connected by either shared protein elements, shared annotating GO terms or based on protein interactions between elements in each protein complex. Second, we propose a simple but effective neighborhood-based algorithm, which yields a local similarity measure, to rank disease candidate protein complexes.ResultsComparing the predictive performance of our proposed algorithm with that of two state-of-the-art network propagation algorithms including one we used in our previous study, we found that it performed statistically significantly better than that of these two algorithms for all the constructed functional similarity protein complex networks. In addition, it ran about 32 times faster than these two algorithms. Moreover, our proposed method always achieved high performance in terms of AUC values irrespective of the ways to construct the functional similarity protein complex networks and the used algorithms. The performance of our method was also higher than that reported in some existing methods which were based on complicated heterogeneous networks. Finally, we also tested our method with prostate cancer and selected the top 100 highly ranked candidate protein complexes. Interestingly, 69 of them were evidenced since at least one of their protein elements are known to be associated with prostate cancer.ConclusionsOur proposed method, including the framework to construct functional similarity protein complex networks and the neighborhood-based algorithm on these networks, could be used for identification of novel disease-protein complex associations.

Project description:BackgroundAbdominal obesity (AO) is linked to reduced health status and mortality. While it is known that AO is prevalent in chronic obstructive pulmonary disease (AO-COPD), the specific metabolic and functional consequences associated with AO-COPD remain understudied.MethodsWe studied 199 older adults with COPD and 168 control subjects with and without AO and assessed visceral adipose tissue (VAT) by dual-energy X-ray absorptiometry. VAT > 70th percentile of the control group qualified a subject as AO in a sex specific manner. We measured plasma concentrations and whole body production (WBP) rates of multiple amino acids to assess the metabolic profile. We assessed medical history, body composition by Dual-Energy X-ray Absorptiometry, muscle strength, and cognitive function. We performed statistics by analysis of covariance (p) and FDR (q) for multiple comparisons.ResultsAO-COPD subjects had 27% more VAT (q < 0.01) than AO-Control subjects despite correction for BMI. Branched-chain amino acid concentrations and WBP rates were generally elevated in AO-COPD but whole body clearance rate was only elevated in COPD. Metabolic syndrome comorbidities (p < 0.01) and systemic inflammation (P < 0.05) were most prevalent in the AO-COPD group. Muscle strength was reduced in COPD subjects (p < 0.001), but partially preserved when combined with AO. Cognitive dysfunction and mood disturbances were present in COPD subjects (p < 0.001) with worst performers in AO-COPD (q < 0.05).ConclusionThe presence of AO is associated with specific metabolic and functional phenotypes in COPD. Clinical trial registry Trial registration ClinicalTrials.gov. In the present paper, we report an analysis of the baseline measurements of COPD subjects and healthy controls from the study numbers: NCT01787682, NCT01787682, NCT02157844, NCT02082418, NCT02065141, NCT02770092, NCT02908425, NCT03159390, NCT02780219, NCT03327181, NCT03796455, NCT04928872, NCT04461236, NCT01173354, NCT01154400.

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Identifying Protein-metabolite Networks Associated with COPD Phenotypes.

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Identifying Protein-metabolite Networks Associated with COPD Phenotypes.

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